Short and LongRun Impacts of Food Price Changes on Poverty

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Short and LongRun Impacts of Food Price Changes on Poverty

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This study uses household models based on detailed expenditure and agricultural production data from 31 developing countries to assess the impacts of changes in global food prices on poverty in individual countries and for the world as a whole. The analysis finds that food price increases unrelated to productivity changes in developing countries raise poverty in the short run in all but a few countries with broadlydistributed agricultural resources. This result

Public Disclosure Authorized Policy Research Working Paper 7011 Short- and Long-Run Impacts of Food Price Changes on Poverty Maros Ivanic Will Martin Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized WPS7011 Development Research Group Agriculture and Rural Development Team August 2014 Policy Research Working Paper 7011 Abstract This study uses household models based on detailed expenditure and agricultural production data from 31 developing countries to assess the impacts of changes in global food prices on poverty in individual countries and for the world as a whole The analysis finds that food price increases unrelated to productivity changes in developing countries raise poverty in the short run in all but a few countries with broadly-distributed agricultural resources This result is primarily because the poor spend large shares of their incomes on food and many poor farmers are net buyers of food In the longer run, two other important factors come into play: poor workers are likely to benefit from increases in wage rates for unskilled workers from higher food prices, and poor farmers are likely to benefit from higher agricultural profits as they raise their output As a result, higher food prices appear to lower global poverty in the long run This paper is a product of the Agriculture and Rural Development Team, Development Research Group It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org The authors may be contacted at wmartin1@worldbank.org The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished The papers carry the names of the authors and should be cited accordingly The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors They not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent Produced by the Research Support Team Short- and Long-Run Impacts of Food Price Changes on Poverty Maros Ivanic and Will Martin World Bank Keywords: Food prices, volatility, poverty, GTAP, households, wages, output, trade JEL: D13, D58, I32 Short- and Long-Run Impacts of Food Price Changes on Poverty In the recent period of high and volatile food prices, there has been considerable concern about impacts of high food prices on global poverty A number of analyses have concluded that, in most developing countries, higher food prices raise the poverty headcount in the short run because not enough poor farming households benefit sufficiently from higher selling prices to offset the negative impacts of higher food prices on net food consumers (Ivanic and Martin 2008; De Hoyos 2011; Ivanic, Martin, and Zaman 2012) This is despite the well-known stylized fact that three-quarters of the world’s poor live in rural areas, and most depend on agriculture for their livelihoods A simple explanation for this apparent contradiction is that many of the poorest farming households are actually net buyers of staple foods Deaton and Laroque (1992) describe the price behavior of storable commodities as being characterized by long periods in the doldrums, punctuated by intense but short-lived price spikes Much of the concern about high food prices in recent years has arisen in a context of intense price spikes such as those in 2007–8 and 2010–11 If the poor are adversely affected by such surges in food prices, and if they have little ability to buffer the effects of these price shocks, then these periods of high prices—whether seen as a problem of high prices or as a problem of volatility—would appear to be a serious concern For some types of price changes, such as those resulting from sustained changes in the global agricultural supply-demand balance of the type that have occurred since 2000, the focus of attention needs to be on the longer-run impacts of price changes In this case, if supply responses are sufficient and/or wage rates for unskilled labor change substantially, the poverty impacts of food price changes might be reversed Clearly, this could have major implications for policy, with policy makers focused on reducing poverty perhaps welcoming, rather than fearing, high food prices Whether this is the case is likely to depend heavily on the income sources of households and the responsiveness of household farm output to price changes, and can only be determined empirically Most of the available analyses of the impacts of food price impacts on poverty follow the classic Deaton (1989) study in taking into account the first-order impacts of higher food prices on household incomes as determined by the initial net-sales position of each household Some, in addition, consider second-order impacts of food prices through changes in the quantities of food demanded, typically finding that including this channel of effect results in small impacts on the estimated poverty effects (Friedman 2002) Others have advanced further on the demand side, assessing the impacts of food price increases on overall spending as households attempt to smooth their consumption; and on the quality, as well as the quantity, of food consumed (Gibson and Kim 2011) and by comparing impacts on quantities of food consumed with impacts on calorie consumption (D'Souza and Jolliffe 2010) Only a few studies of the impacts of food price changes on poverty or nutritional outcomes—such as Mghenyi, Myers an Jayne (2011); Minot and Dewina (2013) and Van Campenhout, Pauw and Minot (2013)— appear to have examined the impacts of food price changes on food output, and hence the longer-term impacts of changes in food prices on the incomes that farmers receive from their farming activities In innovative recent studies, Jacoby (2012) incorporates impacts of higher food prices on wages, while Headey (2013) estimates the total impacts of food price changes on poverty In the short term, we would expect consumers to be able to adjust their food consumption almost fully to minimize the impact on their cost of living but for producers to have relatively limited ability to adjust—if only because of the delays between decisions to commit resources and actual production In the longer-run, farmers can be expected to respond to higher food prices in two distinct ways: (i) by switching agricultural land towards producing those items whose prices have risen relative to others, and (ii) by increasing overall agricultural output through increases in the amount of land allocated to agricultural uses and augmentation of the land available using intermediate inputs and non-land factors In general, there seems likely to be greater flexibility in the response of individual commodities than in total aggregate agricultural output Ideally, we would estimate the parameters needed to take into account household responses to price changes However, this has proved very difficult because most price changes observed in the data are very small and transient relative to the long-run price changes of key interest Attempts at estimation are complicated by the undesirable distributional properties of the widely-used Nerlove model (Diebold and Lamb 1997) To understand the long-run adjustment options of the farmers who face changing output prices, we develop microeconomic simulation models which account for the changes in farmers’ profits as a result of changes in output prices These are specified for consistency with the well-known GTAP model of the world economy so as to allow us to use changes in factor prices generated in that model With those models we are then able to compare short- and long-run poverty impacts of higher food prices, both in the case when the prices of individual crops rise independently and when all food prices rise together When considering the impacts of changes in food prices, it is very important to consider the source of the change A change in food prices resulting from a shock such as a rise in demand for rich-country biofuels that is more or less exogenous to the agricultural sectors of developing countries is likely to be associated with quite different impacts on poverty than one that arises from a change in rates of productivity growth in developing countries—whether from investments in research and development, from weather shocks or from sustained changes in climate For simplicity of interpretation we focus in this paper primarily on changes in poverty that result from price shocks modeled as border price changes not accompanied by changes in domestic agricultural productivity or other domestic policies The effects of price changes on household incomes and on poverty rates are nonlinear for two reasons: (i) because the effects of price changes on real household incomes are nonlinear once we take into account output adjustments, and (ii) the effects of changes in prices on poverty are nonlinear because of differences in the income and expenditure shares of different groups and changes in the number of people near the poverty line Because of these nonlinearities, and because of uncertainty about the size of future price shocks, we consider the impact of price increases over a large range; in particular we consider shocks of 10, 50 and 100 percent To investigate differences between the effects of changes in all agricultural prices and in prices for particular goods, we compare the impacts of increases in all agricultural products with the results obtained when some key prices are increased individually We believe that the approach that we use, involving both economy wide simulation models and household models, is an important complement to studies such as Jacoby (2013) and Headey (2014) It avoids the very real concerns about causality that plague any econometric analysis and allows exploration of the impacts of different sizes and scopes of price changes, and of the impacts through different channels on different types of households If both methodologies provide broadly similar results on key impacts, then we can gradually begin to reduce the uncertainty we face about the important but challenging linkages between food prices and poverty If they not, then we need to look deeper for the causes of the discrepancies between the results emerging from these two different approaches In the next section of this paper, we first consider the methodology used in the analysis Then we turn to the approaches and data used to implement this methodology Next, we present simulations designed to answer key questions about the impact of price changes on poverty Finally we present some conclusions Methodology and Data To distinguish between the impacts of higher food prices in the short and the long run, we need to identify the sources of the differences between the two effects, and to develop expressions which represent each impact In the short run, the first-order impact of a price change is typically captured by the net trade position of the household as specified by Deaton (1989) Even in the short term, however, this expression may need to be augmented to take into account the ability of households to substitute away from goods whose prices have risen, and towards those whose prices have fallen Another factor to consider is the potential impact of higher food prices on the wage rates received by farm household members for labor sold off-farm Since most of the available evidence (see, for example Ravallion 1990; Lasco, Myers and Bernsten 2008) suggests that it takes some time for wages to fully adjust to changes in food prices, this impact is generally not included in analyses focused on short term impacts A third important factor to consider is the ability of farm households to adjust the quantity of food that they produce While full adjustment of output will frequently take a year or more, the elasticities of output supply seem likely to be higher than for food demand, and so these supply-side impacts on income could be substantially larger We first consider the approach used to assess the impact of changes in prices and wage rates on the real income of each household Then, we turn to the approach used to estimate the relationship between food price changes and wages Estimating real income changes at the household level At the household level, we analyze the implications of changes in food prices through a series of micro simulations where we simulate the impact of food price changes and any mitigating responses on each household’s real income To measure the change in welfare of each household, we specify the cost to the household of achieving a given level of utility using a “full” cost function 𝑒(𝒑, 𝑤, 𝑢) of the type discussed by Deaton and Muellbauer (1981) This specifies the minimum cost required by the household to reach utility level u at given a vector of commodity prices, 𝒑, and prices for the factors that it sells, 𝒘 Conventional practice in this type of modeling (see, for example, the studies in Hertel and Winters 2006) is to consider the impacts of changes in prices on the factor returns accruing to households, and to compare these impacts with changes in the cost of living as measured using the expenditure function Given our focus on the effects of changes in prices of the particular— and sometimes quite finely-specified—foods, we adopt a different approach more frequently seen in microeconomic studies of the impact of changing food prices on poverty (see, for example, Deaton 1989) Given the close links—and the substantial evidence of non-separability between firm and household decisions—between the small farm households of most concern to us and their farm businesses, we focus on the impact of price changes on the profits that farming households derive from their own farm firms and the impact of changes in food prices on the wage rates received by household members for sales of labor off-farm While our national models allow for sales and purchases of both skilled and unskilled labor, we focus on changes in the wage rate for unskilled labor 𝑤 and its quantity 𝑙 Given that farm firms are price takers and operate subject to constant returns to scale, the revenues obtained from sales of output (including “sales” to the household for its own consumption) must equal the returns to the factors employed by the firm We also consider the impacts of changes in factor prices on the returns that farm households obtain from their net sales of factors outside the farm firm A Dixit-Norman (1980) style money measure of household welfare W at given utility level, 𝑢,is given by: (1) 𝑊 = −𝑒(𝒑, 𝒘, 𝑢) + 𝜋(𝒑, 𝒘), where 𝑒(𝒑, 𝐰, 𝑢) is the full cost function of the household at any given vector of commodity prices, p, and factor prices, w.; and 𝜋(𝒑, 𝒘) is a profit function representing the profits generated by the farm firm While this measure does not allow for risk aversion, risk preferences could be introduced using a concave, cardinal utility function to evaluate the welfare effects of changes in this real income measure Given the endowments of the poor, we focus only on a single factor price, the wage rate for unskilled labor In the short run, a money measure of the welfare change resulting from a small change in commodity prices and unskilled wage rates is given by (2) ∆𝑊 = −𝑒𝑝 (𝒑, 𝑤, 𝑢)𝑑𝒑 − 𝑒𝑤 (𝒑, 𝑤, 𝑢)𝑑𝑤 + 𝜋𝑝 (𝒑, 𝑤)𝑑𝒑 + 𝜋𝑤 (𝒑, 𝑤)𝑑𝑤, By the envelope theorem, we know that 𝑒𝑝 = 𝒒 , 𝑒𝑤 = −𝑙, 𝜋𝑝 = 𝒙 and 𝜋𝑤 = −𝑦 where 𝒒 is a vector of quantities consumed, −𝑙 is household supply of unskilled labor, 𝒙 is a vector of firm outputs and −𝑦 is the farm firm’s demand for labor This allows us to write a first-order compensating variation measure of welfare change as: (3) ∆𝑊 = −𝒒𝑑𝒑 + 𝑙𝑑𝑤 + 𝒙𝑑𝒑 − 𝑦𝑑𝑤 = (𝒙 − 𝒒)𝑑𝒑 + (𝑙 − 𝑦)𝑑𝑤, The first term in (3), (𝒙 − 𝒒)d𝒑, was used by Deaton (1989) and in many studies (e.g Ivanic and Martin 2008) of the impacts of the 2006–8 food price spike on real household incomes in poor countries, and hence the impacts on poverty The second term in (3), (𝑙 − 𝑦)d𝑤, has been used in studies such as Jacoby (2013) that take into account the impacts of food price changes on wages—and particularly wage rates for unskilled labor resulting from those food price changes When price changes are large enough, and particularly when there is sufficient time for output to adjust, it may be important to take into account higher-order impacts of the price change Expressing the net sale positions of the household for food and for labor as 𝒛𝒑 = (𝒙 − 𝒒), and 𝒛𝑤 = (𝑙 − 𝑦) allows us to develop a second-order Taylor-Series expansion for the welfare change resulting from changes in food prices: (4) ΔW = [𝑧𝑝 𝑧 𝑧𝑤 ] � 𝛥𝒑 � + [𝛥𝒑 𝛥𝑤] � 𝑝𝑝 𝑧𝑤𝑝 𝛥𝑤 𝑧𝑝𝑤 𝛥𝒑 𝑧𝑤𝑤 � �𝛥𝑤 �, This expression takes into account three second-order impacts in addition to the first-order impacts of price changes on welfare The first, 𝛥𝒑′𝑧𝑝𝑝 𝛥𝒑, results from the effect of the output price changes on the supply of commodities It takes into account the fact that the household’s net sales position in a particular commodity will increase if the price of that commodity rises The second, ∆𝑤′𝑧𝑤𝑤 𝛥𝑤 , is the corresponding second-order impact of higher wage rates on the supply of labor to non-farm activities The third, 𝛥𝑤′𝑧𝑤𝑝 𝛥𝒑 + 𝛥𝒑′𝑧𝑝𝑤 𝛥𝑤, combines the impact of the change in commodity prices on the amount of labor sold off farm and the effects of the change in wage rates on farm output To our knowledge, this third term has not previously been taken into account in measuring the impact of food prices on economic welfare Estimating wage impacts of food price changes In order to link changes in food prices with the resulting changes in wage rates for unskilled workers, we use the assumptions, parameters and data of the GTAP model to replicate the GTAP-style nested CES production relationships at a national level with all prices kept exogenous Following the standard GTAP model, we model output as a combination of inputs and value-added in the top nest, and value-added as a combination of factors in the bottom nest Finally, we impose zero profit conditions at each nest and restrict the total quantity of factors available to each country To calculate the implications of the changes in output prices and wage rates on labor 𝑥𝑒 demand and outputs, we express the model equations in their log-linear form as: [𝑀𝑒 |𝑀𝑥 ] �𝑥 � = 𝑥 𝑥𝑒 [0] where �𝑥 � is a stacked vector of endogenous 𝑥𝑒 and exogenous variables 𝑥𝑥 and [𝑀𝑒 |𝑀𝑥 ] is 𝑥 a matrix of coefficients of the equations with block 𝑀𝑒 corresponding to the endogenous variables and block 𝑀𝑥 corresponding to the exogenous variables By solving this equation, we obtain log-linear reduced-form relationships between all exogenous variables (output prices) and endogenous variables (wages etc.) as 𝑥𝑒 = 𝜌𝑥𝑥 where 𝜌 = −𝑀𝑒 −1 𝑀𝑥 is the elasticity matrix Because we are interested in medium- and long-run wage elasticities, we calculate 𝜌 under two sets of assumptions For the medium run, we assume a specific-factors model where capital, land and natural resources are fixed and labor is the only mobile factor For the long-run scenario, we assume that both labor and capital are fully mobile and land is sluggishly adjustable, with an elasticity of transformation equal to one, as assumed in the GTAP model We report the calculated sets of Stolper-Samuelson elasticities of unskilled wage rates with respect to output prices in Appendix tables and Implementing the Approach Ideally one would analyze the impacts of shocks such as food price changes on household incomes using a model in which demands for goods were determined as the total of demands from individual households, and supplies were similarly determined using models of individual producing firms Unfortunately, the development of such a model for our purpose is very challenging at this point given the incomplete coverage of household surveys and differences in Table 6: Estimated Global Poverty Impacts of General Food Price Rises, % Points Scenario 10 percent 50 percent 100 percent 10 percent 50 percent 100 percent 10 percent 50 percent 100 percent 10 percent 50 percent 100 percent 10 percent 50 percent 100 percent 10 percent 50 percent 100 percent 10 percent 50 percent 100 percent Household group Short run All All All Urban Urban Urban Rural Rural Rural Farmer headed Farmer headed Farmer headed Non-farmer headed Non-farmer headed Non-farmer headed Male headed Male headed Male headed Female headed Female headed Female headed 0.8 (0.3) 5.8 (1.3) 13 (2.2) 1.5 (0.2) 9.2 (0.9) 22.5 (1.6) 0.5 (0.5) 4.3 (2.2) 8.9 (3.6) -0.5 (0.4) -0.8 (1.8) 0.1 (2.8) 1.6 (0.3) 9.8 (1.3) 20.8 (2) 0.8 (0.5) 5.7 (2.2) 12.7 (3.6) 1.1 (0.2) 6.7 (0.9) 15.8 (1.3) 27 Short run + wages -1.1 (0.2) -3.9 (0.7) -5.7 (1.2) -0.3 (0.2) 0.2 (0.8) 3.2 (1.2) -1.4 (0.3) -5.7 (1) -9.5 (1.7) -2.1 (0.2) -8.6 (0.9) -13.8 (1.8) -0.5 (0.2) -1 (1) -0.7 (1.8) -1.2 (0.3) -4.3 (1.1) -6.4 (1.9) -0.3 (0.1) -0.4 (0.5) 0.5 (0.8) Medium run Long run -1.2 (0.2) -4.8 (0.7) -7.6 (1.2) -0.4 (0.2) -0.4 (0.8) 1.1 (1.2) -1.6 (0.3) -6.7 (1) -11.4 (1.7) -2.3 (0.3) -9.6 (0.9) -15.2 (1.8) -0.5 (0.2) -1.8 (1) -2.9 (1.8) -1.3 (0.3) -5.2 (1.1) -8.4 (1.9) -0.3 (0.1) -0.8 (0.5) -0.4 (0.9) -1.4 (0.2) -5.8 (0.7) -8.7 (1.3) -0.4 (0.2) -0.6 (0.8) 0.9 (1.3) -1.8 (0.3) -8 (1) -13 (1.8) -2.5 (0.3) -10.9 (1.1) -16.8 (2.2) -0.7 (0.2) -2.6 (1) -3.8 (1.9) -1.5 (0.3) -6.3 (1.2) -9.6 (2.1) -0.4 (0.1) -1.3 (0.5) -1.1 (0.9) Table 7: Global Poverty Impacts of Hundred-Percent Price Increases, % Points Scenario Beef Chicken Dairy Maize Vegetable oils Rice Soybeans Wheat Household group Short run All All All All All All All All 0.1 (0.1) (0.1) 0.9 (0.2) -1.1 (0.3) 1.5 (0.2) 1.9 (0.6) -0.1 (0) 1.3 (0.4) 28 Short run + wages -0.1 (0.1) -0.2 (0.1) -2.1 (0.4) -1.2 (0.3) -0.2 (0.2) -1.1 (0.3) -0.2 (0) (0.3) Medium run Long run -0.1 (0.1) -0.3 (0.1) -2.2 (0.4) -1.6 (0.3) -0.3 (0.2) -3.2 (0.4) -0.2 (0) 0.6 (0.4) -0.2 (0.1) -0.8 (0.2) -2.5 (0.5) -3.4 (0.7) 1.3 (0.4) -5.9 (0.6) -0.1 (0) -1.3 (0.6) Table 8: Weights of Food Components in the World Bank's Food Price Index, in Percent Share 8.5 7.1 11.5 10.1 30.7 9.8 6.8 Rice Wheat Maize Soybeans Vegetable oils Sugar Beef Chicken 29 Table 9: Global Poverty Impacts of Global Price Increases Which Would Raise the World Bank’s Food Price Index by Ten Percent, Percentage Points Scenario Beef Chicken Maize Vegetable oils Rice Soybeans Wheat Household group Short run All All All All All All All 0.2 (0.1) -0.1 (0.1) -1 (0.2) 0.5 (0.1) 2.3 (0.7) -0.1 (0) 1.8 (0.5) 30 Short run + wages -0.1 (0.1) -0.4 (0.1) -1.1 (0.2) -0.1 (0) -1.3 (0.3) -0.2 (0) 1.3 (0.5) Medium run Long run -0.1 (0.1) -0.6 (0.1) -1.3 (0.3) -0.1 (0) -3.9 (0.5) -0.2 (0) 0.7 (0.5) -0.2 (0.1) -1.3 (0.2) -3 (0.6) 0.5 (0.1) -6.5 (0.6) -0.1 (0) -1.7 (0.8) Output B Output A Inputs Inputs Value added Land Capital Value added Land Labor Capital Labor Figure 1: Diagram of household output in the medium run Shaded rectangles denote fixed quantities; broken border denotes fixed prices 31 Labor Output B Output A Inputs Inputs Value added Land Capital Land Land Labor Capital Value added Capital Labor Labor Figure 2: Diagram of household output in the long run Shaded rectangles denote fixed quantities; hatched rectangles denote sluggishly adjusting quantities; broken border denotes fixed prices 32 References Van Campenhout, B., Pauw, K and Minot, N 2013 “The Impact of Food Price Shocks in Uganda: First-Order versus Long-Run Effects.” IFPRI Discussion Paper 01284, International Food Policy Research Institute, Washington DC D'Souza, Anna 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Oxford Economic Papers 42, no 3: 574–85 34 Appendix Appendix table 1: Medium-Run Wage Elasticities with Respect to Output Prices Country Albania Armenia Bangladesh Belize Cambodia China Cote d'Ivoire Ecuador Guatemala India India India Indonesia Malawi Moldova Mongolia Nepal Nicaragua Niger Nigeria Pakistan Panama Peru Rwanda Sierra Leone Sri Lanka Tajikistan Tanzania Timor-Leste Uganda Vietnam Vietnam Yemen Zambia Year 2005 2004 2005 2009 2003 2002 2002 2006 2006 2004 2005 2905 2007 2004 2009 2002 2002 2005 2007 2003 2005 2003 2007 2005 2011 2007 2007 2008 2007 2005 2004 2010 2006 2010 Top commodity milk, 0.2 milk, 0.4 rice, 0.6 oth prc food, 0.4 oth prc food, 0.3 oth prc food, 0.3 oth prc food, 0.3 oth prc food, 0.4 oth prc food, 0.4 oth prc food, 0.3 oth prc food, 0.3 oth prc food, 0.3 oth prc food, 0.3 raw tobacco, 0.3 oth prc food, 0.4 sheep, goats, 0.1 rice, 0.3 oth prc food, 0.3 oth veget., 0.2 cassava, 0.5 raw milk, 0.2 oth prc food, 0.2 oth prc food, 0.3 oth prc food, 0.2 oils and fats, 0.2 oth prc food, 0.4 plant-based fibers, 0.2 oth prc food, 0.5 oth prc food, 0.4 oth prc food, 0.6 oth prc food, 0.4 oth prc food, 0.4 oth prc food, 0.3 oth prc food, 0.6 Second commodity oth prc food, 0.2 oth prc food, 0.3 sugar, 0.2 sugar, 0.2 rice, 0.2 oils and fats, 0.1 coffee, tea, 0.2 rice, 0.2 sugar, 0.1 rice, 0.2 rice, 0.2 rice, 0.2 oils and fats, 0.2 oth prc food, 0.2 oils and fats, 0.2 wool, 0.1 raw milk, 0.1 milk, 0.1 oils and fats, 0.2 oth veget., 0.2 sugar, 0.2 rice, 0.2 milk, 0.1 milk, 0.2 oth prc food, 0.1 rice, 0.3 milk, 0.2 maize, 0.1 rice, 0.3 milk, 0.1 rice, 0.3 rice, 0.3 milk, 0.2 oils and fats, 0.1 35 Rest rest, 0.4 rest, 0.2 rest, 0.4 rest, 0.3 rest, 0.2 rest, 0.2 rest, 0.8 rest, 0.5 rest, 0.4 rest, 0.5 rest, 0.5 rest, 0.5 rest, 0.3 rest, 0.6 rest, 0.6 rest, 0.2 rest, 0.5 rest, 0.4 rest, 0.7 rest, 0.5 rest, 0.7 rest, 0.2 rest, 0.3 rest, 0.4 rest, 0.8 rest, 0.4 rest, 0.9 rest, 0.4 rest, 0.2 rest, 0.5 rest, 0.2 rest, 0.2 rest, 0.3 rest, 0.4 Appendix table 2: Long-Run Wage Elasticities with Respect to Output Prices Country Albania Armenia Bangladesh Belize Cambodia China Cote d'Ivoire Ecuador Guatemala India India India Indonesia Malawi Moldova Mongolia Nepal Nicaragua Niger Nigeria Pakistan Panama Peru Rwanda Sierra Leone Sri Lanka Tajikistan Tanzania Timor-Leste Uganda Vietnam Vietnam Yemen Zambia Year 2005 2004 2005 2009 2003 2002 2002 2006 2006 2004 2005 2905 2007 2004 2009 2002 2002 2005 2007 2003 2005 2003 2007 2005 2011 2007 2007 2008 2007 2005 2004 2010 2006 2010 Top commodity oth prc food, 0.4 milk, 0.8 rice, 0.4 oth prc food, 0.8 oth prc food, 0.5 oth prc food, 0.3 oth prc food, -0.7 sugar, -0.3 milk, 0.3 oth prc food, 0.5 oth prc food, 0.5 oth prc food, 0.5 oils and fats, 0.2 oth prc food, 0.8 oth prc food, 0.3 sheep, goats, 0.2 rice, 0.3 oth prc food, 0.7 oth prc food, -0.6 cassava, 0.7 raw milk, 0.2 oth prc food, 0.3 oth prc food, 0.5 milk, 0.4 oth prc food, -0.6 oth prc food, 0.4 plant-based fibers, 0.4 oth prc food, 0.7 oth prc food, 0.6 oth prc food, 0.9 rice, 0.3 rice, 0.3 oth prc food, 0.7 oth prc food, 0.7 Second commodity raw milk, 0.1 oth prc food, 0.6 sugar, 0.4 sugar, 0.1 rice, 0.2 oils and fats, coffee, tea, 0.4 oils and fats, 0.3 oils and fats, 0.2 rice, 0.2 rice, 0.2 rice, 0.2 rice, 0.2 sugar, 0.5 oils and fats, 0.3 wool, 0.1 oth prc food, 0.1 milk, 0.3 oth veget., 0.3 oth veget., 0.3 oth prc food, 0.2 rice, 0.1 milk, 0.2 oth prc food, 0.1 plant-based fibers, 0.3 rice, 0.4 oth veget., 0.2 rice, 0.1 rice, 0.3 oils and fats, 0.2 oth prc food, 0.2 oth prc food, 0.2 milk, 0.6 oils and fats, 0.2 36 Rest rest, 0.2 rest, 0.2 rest, 0.4 rest, 0.3 rest, 0.1 rest, 0.2 rest, 1.1 rest, 0.9 rest, 0.4 rest, 0.2 rest, 0.2 rest, 0.2 rest, 0.3 rest, 1.1 rest, 0.8 rest, 0.2 rest, 0.5 rest, 0.3 rest, 1.1 rest, 0.5 rest, 0.5 rest, 0.3 rest, 0.4 rest, 0.6 rest, 1.1 rest, 0.5 rest, 0.9 rest, 0.6 rest, 0.1 rest, 0.6 rest, 0.2 rest, 0.2 rest, 0.2 rest, 0.5 Appendix table 3: Average Own-Price Elasticities of Demand by Country Country Albania Armenia Bangladesh Belize Cambodia China Cote d'Ivoire Ecuador Guatemala India Indonesia Malawi Moldova Mongolia Nepal Nicaragua Niger Nigeria Pakistan Panama Peru Rwanda Sierra Leone Sri Lanka Tajikistan Tanzania Timor-Leste Uganda Vietnam Yemen Zambia Year 2005 2004 2005 2009 2003 2002 2002 2006 2006 2005 2007 2004 2009 2002 2002 2005 2007 2003 2005 2003 2007 2005 2011 2007 2007 2008 2007 2005 2010 2006 2010 Top commodity beef, -0.2 oth prc food, -0.2 rice, -0.1 oth prc food, -0.2 oth prc food, -0.1 proc tobacco, -0.2 rice, -0.1 oth prc food, -0.2 maize, -0.1 rice, -0.1 oth prc food, -0.2 maize, -0.1 oth prc food, -0.1 sheep meat, -0.2 rice, -0.1 oth prc food, -0.1 oth prc food, -0.1 rice, -0.1 milk, -0.2 oth prc food, -0.2 oth prc food, -0.2 oth veget., -0.1 rice, -0.1 oth prc food, -0.2 oth prc food, -0.1 oth prc food, -0.1 oth veget., -0.1 oth veget., -0.1 oth beverages, -0.1 proc tobacco, -0.2 oth veget., -0.1 Second commodity oth prc food, -0.2 raw tobacco, -0.1 oth prc food, -0.1 proc tobacco, -0.2 rice, -0.1 pork, -0.2 cassava, -0.1 oth beverages, -0.2 oth prc food, -0.2 milk, -0.2 rice, -0.1 oth veget., -0.1 milk, -0.2 milk, -0.2 milk, -0.2 milk, -0.2 oth grains, -0.1 cassava, -0.1 wheat, -0.1 milk, -0.2 wheat, -0.1 oth prc food, -0.1 fishing, -0.1 rice, -0.1 oth veget., -0.1 maize, -0.1 rice, -0.1 plantains, -0.1 rice, -0.1 wheat, -0.1 oth prc food, -0.1 37 Third commodity oth veget., -0.1 wheat, -0.1 fishing, -0.2 chicken meat, -0.2 fishing, -0.1 oth prc food, -0.2 fishing, -0.2 oth veget., -0.1 oth veget., -0.1 oth prc food, -0.1 proc tobacco, -0.2 pork, -0.2 oth veget., -0.1 oth prc food, -0.2 oth veget., -0.1 proc tobacco, -0.1 beef, -0.2 oth veget., -0.1 oth oil seeds, -0.1 chicken meat, -0.2 milk, -0.2 proc tobacco, -0.1 oth oil seeds, -0.1 fishing, -0.2 oth fruits, -0.1 oth veget., -0.1 maize, -0.1 oth prc food, -0.1 pork, -0.2 oth veget., -0.1 fishing, -0.2 Appendix table 4: Medium-Run Supply Elasticities by Commodity, Medians over Households Country Albania Armenia Bangladesh Belize Cambodia China Cote d'Ivoire Ecuador Guatemala India Indonesia Malawi Moldova Mongolia Nepal Nicaragua Niger Nigeria Pakistan Panama Peru Rwanda Sierra Leone Sri Lanka Tajikistan Tanzania Timor-Leste Uganda Vietnam Yemen Zambia Year 2005 2004 2005 2009 2003 2002 2002 2006 2006 2005 2007 2004 2009 2002 2002 2005 2007 2003 2005 2003 2007 2005 2011 2007 2007 2008 2007 2005 2010 2006 2010 Top commodity raw milk, 0.8 cattle, 0.9 rice, rice, 1.3 oth crops, 0.2 fishing, 0.5 cassava, 0.9 oth crops, 0.4 maize, 0.4 rice, 1.7 rice, 1.1 maize, 0.5 poultry, 1.3 raw milk, 0.5 rice, 1.1 oth crops, 0.3 sheep, goats, 3.5 cassava, 0.8 raw milk, 0.3 cattle, 0.3 oth fruits, 0.5 oth veget., 0.9 rice, 4.8 rice, 0.8 oth crops, 0.5 maize, 0.5 oth veget., 0.2 oth veget., 0.8 rice, 1.5 oth crops, 0.5 oth veget., 0.4 Second commodity cattle, 0.8 raw milk, 0.7 fishing, 0.1 fishing, 0.1 rice, 1.3 swine, 0.7 coffee, tea, oth veget., 0.5 cattle, 0.4 wheat, 0.4 oth crops, 0.2 oth veget., 0.5 oth veget., 0.8 sheep, goats, 0.4 raw milk, 0.6 oth veget., 0.3 oth grains, 1.7 oth veget., 0.8 wheat, 0.3 raw milk, 0.3 raw milk, 0.9 sorghum, 1.1 oth oil seeds, coffee, tea, 0.2 oth veget., 0.8 oth veget., 0.4 maize, 0.2 plantains, 0.8 fishing, 0.1 fishing, 0.1 maize, 0.6 38 Third commodity oth veget., 0.4 oth fruits, 0.4 oth veget., 0.3 oth fruits, 0.4 fishing, 0.1 rice, 1.7 oth veget., 0.9 oth fruits, 0.5 oth veget., 0.4 raw milk, 0.2 oth fruits, 0.2 raw tobacco, raw milk, 0.8 oth anim prod., 0.5 oth veget., 0.4 rice, 1.3 oth veget., fishing, cattle, 0.4 poultry, 0.3 rice, 1.9 cattle, 1.8 fishing, 0.4 oth veget., 0.2 oth fruits, 0.8 rice, coffee, tea, 0.2 maize, 0.7 swine, 0.4 sheep, goats, 0.7 oth crops, 0.5 Appendix table 5: Long-Run Supply Elasticities by Commodity, Medians over Households Country Albania Armenia Bangladesh Belize Cambodia China Cote d'Ivoire Ecuador Guatemala India Indonesia Malawi Moldova Mongolia Nepal Nicaragua Niger Nigeria Pakistan Panama Peru Rwanda Sierra Leone Sri Lanka Tajikistan Tanzania Timor-Leste Uganda Vietnam Yemen Zambia Year 2005 2004 2005 2009 2003 2002 2002 2006 2006 2005 2007 2004 2009 2002 2002 2005 2007 2003 2005 2003 2007 2005 2011 2007 2007 2008 2007 2005 2010 2006 2010 Top commodity raw milk, 6.2 cattle, 5.6 rice, 5.2 rice, 7.3 oth crops, 1.5 fishing, 0.5 cassava, 8.5 oth crops, maize, 2.8 rice, 5.3 rice, 2.9 maize, 5.1 poultry, 12.3 raw milk, 3.2 rice, oth crops, 2.9 sheep, goats, 27.9 cassava, 5.4 raw milk, 1.4 cattle, 3.7 oth fruits, 2.5 oth veget., 9.6 rice, 51.2 rice, 2.4 oth crops, 2.5 maize, 6.6 oth veget., 1.8 oth veget., 8.2 rice, 4.8 oth crops, 6.2 oth veget., Second commodity cattle, 4.4 raw milk, 3.6 fishing, 0.4 fishing, 0.3 rice, swine, 5.9 coffee, tea, 8.4 oth veget., 6.5 cattle, 4.7 wheat, 4.6 oth crops, 1.2 oth veget., 6.7 oth veget., 7.1 sheep, goats, 2.6 raw milk, 5.1 oth veget., 4.2 oth grains, 14.2 oth veget., 7.9 wheat, 3.3 raw milk, 3.6 raw milk, 5.7 sorghum, 10.1 oth oil seeds, 23.8 coffee, tea, 0.8 oth veget., 6.6 oth veget., 5.1 maize, 1.9 plantains, 8.6 fishing, 0.4 fishing, 0.3 maize, 39 Third commodity oth veget., 2.8 oth fruits, 2.1 oth veget., 2.3 oth fruits, 2.1 fishing, 0.4 rice, 7.3 oth veget., 11.2 oth fruits, oth veget., 5.5 raw milk, 1.3 oth fruits, 1.4 raw tobacco, 17.9 raw milk, 6.5 oth anim prod., 3.2 oth veget., 3.7 rice, oth veget., 7.6 fishing, 0.1 cattle, 3.9 poultry, 4.5 rice, 5.9 cattle, 13.7 fishing, 0.5 oth veget., 0.9 oth fruits, 6.5 rice, 11.6 coffee, tea, 2.2 maize, 7.7 swine, 3.2 sheep, goats, 7.2 oth crops, 6.7 Appendix table 6: Median Aggregate Agricultural Supply Elasticities, at Household Level Country Albania Armenia Bangladesh Belize Cambodia China Côte d'Ivoire Ecuador Guatemala India Indonesia Malawi Moldova Mongolia Nepal Nicaragua Niger Nigeria Pakistan Panama Peru Rwanda Sierra Leone Sri Lanka Tajikistan Tanzania, United Republic of Timor-Leste Uganda Viet Nam Yemen Zambia Year 2005 2004 2005 2009 2003 2002 2002 2006 2006 2005 2007 2004 2009 2002 2002 2005 2007 2003 2005 2003 2007 2005 2011 2007 2007 2008 2007 2005 2010 2006 2010 40 Medium-run 0.8 0.6 0.4 0.4 0.4 0.6 1.3 0.4 0.3 0.3 0.2 0.5 0.8 0.5 0.4 0.3 1.4 0.8 0.2 0.3 0.6 1.9 0.2 0.9 0.4 0.2 0.5 0.4 0.6 0.4 Long-run 0.8 0.8 0.8 0.4 0.8 0.8 3.5 0.8 0.4 0.5 0.3 0.6 1.4 0.5 0.6 0.3 3.4 0.9 0.3 0.3 0.7 11.7 0.2 2.1 0.5 0.3 1.4 0.6 0.7 0.7 -1 -1 .5 -.38 -.18 -.68 -.25 -.69 -.75 -.31 -.38 -.31 -.44 -.31 .68 -.25 .25 -1 -1 41 -1 -1 Equation for quantity of land in wheat production Equation for quantity of natural resources in rice production Equation for quantity of natural resources in wheat production Equation for quantity of value added in rice production Equation for quantity of value added in wheat production Equation for quantity of land in rice production Equation for quantity of labor in wheat production Equation for quantity of labor in rice production 93 07 Equation for natural resources transformation in rice production Equation for natural resources transformation in wheat production Equation for output zero profits in rice production Equation for output zero profits in wheat production Equation for value added zero profits in rice production Equation for value added zero profits in wheat production Equation for quantity of capital in rice production Equation for quantity of capital in wheat production Equation for quantity of inputs in rice production Equation for quantity of inputs in wheat production -1 .93 07 Equation for natural resources supply Equation for land supply Price of capital Price of inputs Price of labor Price of land in everything Price of land in rice production Price of land in wheat production Price of natural resources in everything Price of natural resources in rice production Price of natural resources in wheat production Price of output in rice production Price of output in wheat production Price of value added in rice production Price of value added in wheat production Quantity of capital in rice production Quantity of capital in wheat production Quantity of inputs in rice production Quantity of inputs in wheat production Quantity of labor Quantity of labor in rice production Quantity of labor in wheat production Quantity of land in rice production Quantity of land in wheat production Quantity of natural resources in rice production Quantity of natural resources in wheat production Quantity of output in rice production Quantity of output in wheat production Quantity of value added in rice production Equation for land transformation in rice production Equation for land transformation in wheat production Equation for labor supply 97 03 variable Equation for capital supply Appendix table 7: Example of a Household Model (a Sample Household From Bangladesh, 2005 Survey) -.68 -.25 -.68 -.25 -.68 -.25 68 68 68 25 25 25 -1 -1 -1 -1 -1 -1 -1

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Mục lục

  • Short- and Long-Run Impacts of Food Price Changes on Poverty

  • Methodology and Data

    • Estimating real income changes at the household level

    • Estimating wage impacts of food price changes

  • Implementing the Approach

    • Measuring aggregate poverty levels

  • Data

  • Simulations

    • Poverty Impacts of Price Increases

    • Poverty implications of individual food price increases

  • Conclusions

  • Appendix

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